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Objective:To develop an program for training diabetes educators amid normalization stage of novel coronavirus pandemic prevention and control, and provide reference for clinical teaching under special circumstances.Methods:From January 2020 to June 2021, The head member of Diabetes Education and Management Committee of Hunan Health Management Association Xiangya Hospital of Central South University, developed and implemented a program based on core competencies of diabetes educators. It included 26 online courses, one to one education demonstration video with 7 themes, one diabetes conversation map and simulation, and three-week clinical practice. From April 2021 to June 2021, 55 health workers participated in the program. Theoretical tests, video feedback and questionnaires were used to evaluate the trainees′ diabetes knowledge, educational skills and satisfaction with the training program.Results:A total of 55 health workers participated in the program. All the participants completed the online courses and passed the quizzes, the theoretical examination score was (92.93 ± 5.28). Fifty-four participants finished the tasks of clinical practice and obtained the certificate of diabetes educator. Their communication skills and strategies of behavior change were significantly improved ( χ2 values were 4.17 -26.34, all P<0.05), and all of them were satisfied with the program. Conclusions:The training mode based on online training is flexible, convenient and effective,which is suitable for stage of novel coronavirus pandemic prevention and control.
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OBJECTIVES@#The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification.@*METHODS@#The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model.@*RESULTS@#This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm.@*CONCLUSIONS@#The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
Subject(s)
Humans , Artificial Intelligence , Diabetes Mellitus , Diabetic FootABSTRACT
OBJECTIVE@#To explore the effectiveness of systematic self-management education on blood sugar level of patients in the community with type 2 diabetes.@*METHODS@#A total of 248 patients with type 2 diabetes from 10 communities of Changsha were randomized into intervention or control groups in February 2009. The intervention group received systematic self-management education, while the other received routine community education. Before and after intervention, fasting plasma glucose, 2 h postprandial plasma glucose and glycosylated hemoglobin were measured, and evaluated between the 2 groups.@*RESULTS@#Fasting plasma glucose, 2 h postprandial plasma glucose and glycosylated hemoglobin levels of patients with diabetes from the two groups were generally comparable at baseline (P>0.05). After the intervention, these three indicators were all statistically significant between the 2 groups (P0.05), 2 h plasma glucose and glycosylated hemoglobin levels were improved (P<0.01, P<0.05, respectively).@*CONCLUSION@#Systematic self-management education effectively encourages patients with type 2 diabetes to control their blood sugar levels, and deserves further promotion.
Subject(s)
Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Blood Glucose , China , Diabetes Mellitus, Type 2 , Blood , Therapeutics , Health Education , Methods , Patient Education as Topic , Methods , Self Care , Methods , Self ReportABSTRACT
OBJECTIVE@#To explore the effect of systematic self-management education on quality of life, anxiety and depression of patients with Type 2 diabetes in communities.@*METHODS@#A total of 248 patients with Type 2 diabetes from 10 communities of Changsha from February 2009 to July 2010 were randomized into an intervention group and a control group. The intervention group received systematic self-management education, while the controls received routine community education. The quality of life, anxiety and depression were measured by adjusted diabetes-specific quality of life scale (A-DQOL), self-rating anxiety scale (SAS) and self-rating depression scale (SDS) respectively.@*RESULTS@#The quality of life, morbidity of anxiety or depression of the intervention and the control group were comparable at the baseline (P>0.05). One and half year later, there was significant difference between the two groups (P<0.01). Compared with the control group, the intervention group got better quality of life (P<0.01), less morbidity of anxiety (8.94% vs. 44.4%, P<0.01) and depression (23.58% vs. 56.00%, P<0.01).@*CONCLUSION@#Systematic self-management education can effectively improve the quality of life, reduce the level of anxiety and depression of patients with Type 2 diabetes in communities, which deserves further generalization.